The inordinately well-financed
Center for Science and Culture of the Discovery Institute of Seattle is the home
of the new anti-evolution gang. They fight for modifying the school curricula by
inserting creationism as an alternative to evolution, or for what they
euphemistically call “teaching the controversy,” yet shrug off the label of
creationism, calling themselves instead Intelligent Design (ID) theorists.

Repeated
defeats of creationists by the US legal system has forced them to regroup and
look for new strategies. ID advocates sport scientific degrees from good
universities and often display substantial erudition and seeming sophistication
much exceeding that of earlier creationists. Since ID purports to be a
scientific enterprise, they need flag bearers with seemingly impressive
scientific credentials, if not actual scientific achievements. Foremost among
IDers is William A. Dembski, with a long list of degrees including a Ph.D. in
mathematics, a Ph.D. in philosophy, and a Master’s degree in theology.1

Dembski’s many
degrees and scores of published books and papers cannot conceal, however, that
he has never conducted real scientific research. Moreover, Dembski’s literary
production contains no real mathematics but instead a lot of philosophizing,
often saturated with unnecessary mathematical symbolism. As his extensive
literary production is critiqued by experts, Dembski, without admitting errors,
often surreptitiously shifts his position. These tactics may be handy if winning
the battle regardless of means is the only goal, but they also lead to the
inconsistency that has become Dembski’s trademark.

In this
article I shall concentrate on the most salient features of Dembski’s prolific
literary output, almost all of which turns out to be poorly substantiated,
contradictory, and often self-aggrandizing.

Dembski’s Literary Output, Its Admirers and Detractors

Dembski’s
co-travelers frequently and excessively acclaim his works as great breakthroughs
in science, even to the extent of comparing him to Isaac Newton.2 His
works, however, have also been extensively criticized.3 Dembski’s
latest book is The Design Revolution: Answering the Toughest Questions about
Intelligent Design.4 Contrary to what this title might suggest,
we find in that book’s Index none of the names of his harshest critics.
Dembski’s book more properly should have been subtitled Dodging
the Toughest Questions about Intelligent
Design.

Dembski is
selective in deciding which critique to respond to and which to ignore. For
example, his (mis)use of the No Free Lunch (NFL) theorems (discussed below) was
subjected to a strong critique by Wein5 and Wolpert.6 In
two lengthy rebuttals7 Dembski spared no effort to reply to Wein, but
he never uttered a word in response to Wolpert. It is not hard to understand
why. Wein, as Dembski stresses in his posts, has only a bachelor’s degree in
statistics. This irrelevant factoid, according to Dembski, makes Wein
insufficiently qualified to dispute Dembski’s work (in fact Dembski evaded
answering the substance of Wein’s well justified critical comments). Dembski
could not use such silly arguments against Wolpert, because Wolpert is a highly
respected mathematician and a co-author of the very NFL theorems Dembski
misuses.

I also have
written at length about Dembski’s ideas.8 Dembski, who is aware of my
critique9 has never responded to it. In scientific debates, if one of
the disputants fails to respond, it is usually construed as a silent admission
of errors.

If Dembski
responded to my critique, he could say that I unduly simplify his sophisticated
arguments, thus depriving them of their real deeper meaning. Indeed, I
deliberately simplify his arguments because to my mind all their seeming
sophistication is just a smoke screen intended to make his often hackneyed
notions look like important insights where there is none. I see one of my tasks,
when discussing Dembski’s work, as removing its veneer of sophistication and
laying bare his real underlying notions. \

Explanatory Filter

Dembski’s
Explanatory Filter (EF) is, he claims, a reliable tool for discriminating
between those events that are results of “Intelligent Design” and those that
happen either by chance or due to necessity (also called “law” or “regularity”)
(see Figure 1). He has published a description of the EF in four books and a
number of articles,10 not to mention frequent posts to the Internet,
so this is obviously an important component to his ID theory. So far, however,
there are no reported instances of a successful application of the EF by
anybody, including Dembski’s colleagues or Dembski himself, to any specific
problem where design may be suspected as an event’s antecedent. It is not hard
to understand why.

According to
Dembski, there are three and only three clearly distinctive categories of
antecedent factors (causes) for any event: necessity (also called law or
regularity), chance, and Intelligent Design. The term “intelligent” in Dembski’s
interpretation does not necessarily imply that the designer possesses a high
intelligence; he (she, it?) even can be stupid;11 the term
“intelligent” implies only that, regardless of its optimality, design is a
product of an “intelligent agent” rather than of chance or necessity.

Many events
result from a combination of more than one cause.12 The notion of
only three separate causes does not jibe with reality. Consider Dembski’s own
favorite case of an archer shooting arrows at a target painted on a wall.
According to Dembski, if the archer hits the bull’s-eye, it is a result of
design (the archer’s skill) and only of design. In fact, the archer’s
skill (design) assures only a certain velocity of the arrow when it leaves the
bow. To hit the bull’s-eye, the arrow must also follow the laws of mechanics,
which determine its trajectory from the bow to the target. Hence, if the arrow
hits the bull’s-eye, it is the result of a combination of design and regularity
(law, necessity). Similarly, because of an occasional gust of wind, chance also
may become a factor. All three causes can act simultaneously.12

Dembski’s EF
does not account for events that have more than one cause, and that is one of
the reasons EF is an inadequate tool for the design inference. There are,
however, more serious fallacies in the EF.

The EF (Figure
1) is a flow-chart comprising three “nodes” that correspond to three steps
employed to decide whether an event is due to chance, necessity, or design. In
fact, of the three “nodes,” the first and the second cannot be practically used.
According to Dembski, in the first node of the EF, the probability of the event
is to be estimated. If it is found to be “large” (Dembski offers no quantitative
bounds for such a determination) the event is attributed to law (necessity,
regularity); if the probability is not “large,” necessity is rejected, and the
event passes to the second node of the EF.

In Dembski’s
imaginary procedure, an event is attributed to necessity (law, regularity)
because its probability is found to be “large.” Since probability can’t be
“read off the event,” in practice we can only do it in the reverse order—first
determine that the event is caused by necessity (law, regularity) and
therefore conclude that its probability is large.

Likewise in
the second node of the EF, Dembski’s schema requires us to re-estimate the
event’s probability. If it is found to be “intermediate” (Dembski again provides
no quantitative bounds for such a determination), the event is attributed to
chance. If, though, the probability in question is “small,” the event passes to
the EF’s third node. However, to estimate the probability now, we need certain
knowledge about the event’s causal history, as we did in the first node.
Dembski’s schema prescribes the unrealistic, sequence of steps—an event’s
probability is found to be intermediate (how?); therefore we
attribute it to chance. This schema is impossible to apply, because we can’t
read probability “off the event.” Since the first and the second nodes of
Dembski’s EF make no sense, the EF cannot be used in real-life situations.

In the third
node of the EF, the final choice is to be made between chance and design.
(Again, the possibility of a combination of causes is ignored, as well as the
not-so-rare situations wherein no attribution is feasible because of the paucity
of information.) Dembski’s prescription here differs from the two preceding
nodes. First, the probability of the event is estimated assuming it
happened by chance (the procedure here is opposite to that suggested for the
first and the second nodes). If this probability is not very “small,” the event
is attributed to chance.

Dembski has
suggested a Universal Probability Bound (UPB)13 which he chose to be
UPB=1/2×10-150. This threshold translates into about 500 bits of
information (discussed later). If the probability of a specified event is
less than the UPB, that is, if the information associated with this event is
over 500 bits, the event, according to Dembski, cannot be attributed to chance.

I will not
discuss here Dembski’s reasoning for his choice of the particular value of UPB
(although this reasoning has a number of dubious elements) because this value
per se is not of a principal significance for this discourse.

Regarding how
small the probability has to be to qualify as a condition for a design
inference, Dembski is inconsistent. On the one hand, he seems to prescribe using
his UPB as the threshold of a sufficiently small probability. On the other hand,
in many of his examples, he views the probability as small enough for inferring
design even when it is by orders of magnitude larger than the UPB; for example,
he considers a seven-digit phone number as sufficiently improbable to justify a
design inference although the probability of this number is immensely larger
than the UPB.14

According to
Dembski, if the probability of a chance occurrence of an event is found to be
small, then the event must next be tested for specification. Let us
construe specification in its most common sense—as a choice of an object out of
a set of similar objects. For example, if you are asked to pull “a card” from a
deck, the card is not “specified.” Any card you randomly choose will do. If,
though, you are asked to pull the seven of spades from a deck, this time the
card is specified, and only the seven of spades will do. This simple example
shows the commonly understood difference between specified and unspecified
objects.

What is the
probability that whichever card you randomly choose will turn out to be “a
card”? Obviously this probability is p=1 (or 100%), because any card you choose
meets the definition of being “a card.” What is the probability that the card
you randomly pull from a deck will turn out to be the seven of spades? Since
there are 52 cards in the deck, each having the same chance of being randomly
chosen, the probability in question this time is 1/52. Hence, in this example
specification makes the event’s probability 52 times lower than for an
unspecified event.

In general,
specification always decreases the probability of an event. There is no basis
for construing specification as a category qualitatively independent of the
event’s probability (as Dembski suggests). Specification (if any is detected)
adds only quantitatively to the probability estimate but warrants no
qualitatively different contribution to the putative design inference.

Dembski
imposes certain restrictions on specification if it is to be used for a design
inference15. Therefore Dembski’s specification is a narrower concept
than the one used in the discussion above. Not all events that are specified in
the above broad sense are specified in Dembski’s sense. However, all events that
are specified in Dembski’s sense all are also specified in the above broader
sense. Therefore, applying the concept of specification only in Dembski’s
narrower sense does not invalidate the assertion that specification
quantitatively decreases the event’s probability rather than adds a
qualitatively different factor to the design inference.

Therefore the
procedure suggested by Dembski for the third node of his EF boils down to the
estimate of the event’s probability, either directly or disguised as
specification. The design inference is thus reduced to an argument from
improbability.

Furthermore,
probability is a quantity whose estimate, as Dembski himself asserts,16
is determined by the knowledge about the event in question (in Dembski’s
parlance, by the “background information H”). Obviously the other side of the
coin is the assertion that the estimated probability reflects the level of our
ignorance about the event. Dembski’s discourse, including his EF, is just a
feebly disguised argument from ignorance; its other name is the
God-of-the gaps argument, which has lost credibility even among Dembski’s
philosophical colleagues.17

Dembski admits
that EF can produce false negatives, that is, fail to detect ID where ID is in
fact present. He insists, however, that his EF does not produce false positives,
that is, if it detects design, this result is beyond doubt. To prove this
assertion, Dembski offers two lines of proof.

1. Dembski’s
first proof of EF’s reliability in regard to false positives is a
“…straightforward inductive argument: in every instance where the explanatory
filter attributes design and where the underlying causal history is known, it
turns out design is present; therefore design is actually present whenever the
explanatory filter attributes design.”18

First, as
philosopher Dembski must know, if A entails B, B does not necessarily entail A.
Even if his assertion (that the explanatory filter correctly infers design
whenever an event is known to be caused by ID) were true, that fact in itself
would not necessarily lead to the reverse conclusion (that each time explanatory
filter attributes design, intelligence is indeed the causal antecedent of the
observed event). In fact, though, Dembski does not substantiate even the
underlying statement (for example, by providing a more or less extensive record
of those situations where the causal history is known and filter infers design).
He discusses just a few examples, and we cannot know whether or not these
examples were selected at random or chosen deliberately (cherry-picked) because
they seemed to fit his thesis. Moreover, this alleged proof can be shown to be
false by simply pointing to cases where EF clearly produces false positives.
There are many examples of such false positives19 and we will discuss
one more (the case of triangular snowflakes) below.An early example of false positives came from the ID camp itself, from a
prominent proponent of ID, philosopher of science Del Ratzsch.20
Dembski never addressed Ratzsch’s example; neither did he acknowledge the
examples of false positives suggested by other critics of his work, while
continuing to insist that his EF never produces false positives.21

2. Dembski
second “proof” of the EF’s reliability is: “The Explanatory Filter is a reliable
criterion for detecting design because it coincides with how we recognize
intelligent causation generally.”22 If this is so, why do we need EF
as we can detect design without it, and know how to do it, and do it
“generally”? On the other hand, if his EF is indeed a novel tool for detecting
design, which is superior to “how we do it generally,” how can its
reliability be asserted by comparing it to an inferior procedure used
without it?

Both “proofs”
of EF’s reliability suggested by Dembski are in fact not convincing.

Specified Complexity

Dembski pays much attention to
specification as such, apart from its role in the EF, and he employs a number of
alternative terms, such as Complex Specified Information (CSI), Specified
Complexity (SC), and sometimes simply specification, often shifting the meaning
he attributes to these terms.

When using the
term specification, Dembski explains that it means a certain type of
pattern.23 To serve as a specification, says Dembski, the pattern
must meet a set of additional requirements. The most important among those
requirements is perhaps “detachability.” For example, if you see a heap of
stones arranged in a certain pattern that is not familiar to you, this pattern
is not “detachable” from this specific heap of stones—it does not match any
image you have antecedently stored in your memory. If, though, an astronomer
comes across the same heap of stones, and recognizes in it a pattern reproducing
the shape of a certain constellation, the image of which is familiar to him,
then for him this pattern is “detachable” from the particular heap of stones and
serves as a specification (that is, can lead to the design inference: the
conclusion that some intelligent agent has intentionally placed the stones in
the image of a constellation).24 This example, however, shows that
Dembski’s specification is no more than a subjective recognition of the
pattern. (In this example, as well as in many other instances discussed by
Dembski, the probability that the pile of stones has the shape of a
constellation may be many orders of magnitude larger than UPB=1/2 ×10-150
; this is just one of the many examples of Dembski’s inconsistency.) Another
necessary component of specification, according to Dembski, is complexity,
which he construes as tantamount to low probability: “Probability measures are
disguised complexity measures…with the disguise involving nothing more than a
change in direction and scale.” Similarly, “the greater the complexity, the
smaller the probability”25

I believe that
the very concept of complexity as disguised improbability is contrary to facts
and logic. For example, under certain (rare) weather conditions, an unusual
triangular shape of snowflakes can be observed.26 Unlike more common
forms of snowflakes with their intricately complex structure, these rare
snowflakes have a simple structure. As Dembski asserted,27 snow
crystals’ shapes are due to necessity—the laws of physics predetermine their
appearance. However, triangular snowflakes, while indeed predetermined by laws
of physics, occur only under certain weather conditions, which are very rare and
unpredictable. Therefore we have to conclude that the emergence of the
triangular snowflakes is a random event. This is another example where at least
two causal antecedents—chance and law—are in play simultaneously.

Since the
appropriate weather conditions occur very rarely, the probability of the chance
emergence of the triangular snowflakes is very small; also, they have a uniquely
specific shape. Hence, according to the EF, these snowflakes were
deliberately designed. The more reasonable conclusion, however, is that they
appeared by chance (plus the necessary contribution of law). (This is also
another example of a false positive produced by the EF.) Since the probability
of the occurrence of these snowflakes is small, then, according to Dembski’s
insistence that large complexity is equivalent to low probability, their
complexity must be large. In fact, though, the rare triangular snowflakes have
the simplest form among all the snowflakes observed. Thus, Dembski’s thesis
asserting that complexity is tantamount to small probability is an
unsubstantiated and therefore misleading suggestion.

Complex Specified Information

Dembski’s
Complex Specified Information (CSI) is in fact a combination of low probability
(which he construes as tantamount to complexity) with a recognizable pattern.
For example, according to Dembski, a string of gibberish displays no CSI, even
if its spontaneous emergence has a very low probability, but a segment of a
meaningful text possesses CSI because not only is its spontaneous emergence
improbable but it also displays a recognizable pattern.

where I stands for information
associated with an individual event E, p is the probability of that event, and
the logarithm is to the base of 2. In information theory, I is often called
surprisal; another, more recent term is self-information.29

The definition
(1) simply expresses probability in a logarithmic form. In this rendition, the
concept of information contains nothing beyond the concept of
probability.

If a
definition has been selected, it has to be applied consistently. However, having
chosen (1) as a definition of information, a few pages further30
Dembski refers to the same quantity I as complexity. If I is complexity,
then (1) contradicts Dembski’s own earlier definition of complexity31
as a measure of a difficulty of solving a problem, since (1) has nothing to do
with the difficulty of a problem.

(b)
Complexity, as we have seen, in Dembski’s view is the equivalent of low
probability.

Hence, all
three components of CSI are in fact just components of the overall probability
of the event whose causal history is in question. Dembski’s use of the concept
of CSI, and with it his “complexity-specification criterion,”32 are
just a re-phrased argument from improbability, or, therefore, as noted
above, an argument from ignorance, to which Dembski has added no novel
features besides unnecessary mathematical symbolism.

The Law of Conservation of Information

Dembski’s penchant for
idiosyncratic terms and allegedly revolutionary novel concepts perhaps finds its
most salient expression in his Law of Conservation of Information (LCI). Experts
in information theory have so far paid no attention to Dembski’s supposedly
revolutionary breakthrough,2 so that there are no references to
Dembski’s LCI in any books or papers professionally dealing with information
theory.

The most
concise rendition of LCI by Dembski is perhaps :“Natural causes are incapable
of generating CSI.” And Dembski’s first corollary: “The CSI in a closed
system of natural causes remains constant or decreases,”33 may
serve as an alternative rendition of LCI insofar as it is relevant to our
discussion.

Dembski does
not define “closed system of natural causes.” In particular, it is unclear
whether it includes human intelligence, which obviously can generate CSI but
usually is not considered supernatural. Regardless, I will show that LCI, if
followed consistently using Dembski’s notions, contradicts the second law of
thermodynamics. (Apparently not satisfied with introducing a new “law” regarding
information, Dembski suggests that his LCI can be expanded to become the Fourth
Law of Thermodynamics.34)

Let us see
whether Dembski’s various statements, if applied consistently, lead to a
conclusion compatible with the second law of thermodynamics. To this end, let
us juxtapose several of Dembski’s statements relevant to his LCI.

(a) Dembski uses the concept of entropy H according to
Shannon’s information theory, asserting that “the average information per
character in a string is given by entropy H.”35 That is,
Dembski maintains that

average
information = entropy.

(b) CSI, by definition, is a combination of three components: information,
specification, and complexity.

From (a) and
(b) follows that the behavior of CSI (which includes information as its
necessary component) must not contradict those laws which determine the behavior
of entropy. Entropy obeys the second law of thermodynamics according to which
in a closed system entropy cannot spontaneously decrease. Since Dembski
accepts that “entropy = average information” (see above), he must conclude that
average information associated with a closed system cannot spontaneously
decrease; it can only increase or remain unchanged.

A
thermodynamically closed system, by definition, does not exchange matter and/or
energy with its surrounding. A system closed in the informational sense does not
exchange information with its surrounding. Although matter/energy and
information are not the same, the behavior of both in many respects can be
characterized by the same quantity—entropy. The units used for thermodynamic
entropy differ from informational entropy, but that is due only to convenience;
entropy is essentially a dimensionless quantity36 whose behavior is
determined by the same laws both for thermodynamic and informational entropy.

Hence, while
Dembski’s LCI asserts that CSI cannot increase in his “closed system of
natural causes,” his other notions, if combined with the second law of
thermodynamics, assert that average information (a.k.a. entropy) in a closed
system cannot decrease. These two statements are incompatible.

Specification—one of the components of CSI—is, in Dembski’s rendition, a
qualitative concept. An event can either be specified or not; there are no
degrees of specification (see, however, another view37). Two other
components of CSI—information and complexity—are quantitative; however
complexity, according to Dembski, is just a property of information when the
latter exceeds about 500 bits. Therefore the increase or decrease of CSI
necessarily implies the increase or decrease of its constituent information
(because the third component of CSI—specification, is only a qualitative
concept). Hence, if we talk about an increase or decrease of CSI, then, in
accordance with Demsbki’s concepts, we necessarily talk about an increase or
decrease of information, and therefore of entropy (which is just average
information – see above).

Whereas the
second law of thermodynamic prohibits entropy’s decrease, Dembski’s LCI allows
for its decrease but prohibits its increase. I see no way to reconcile Dembki’s
LCI with the second law of thermodynamics. This seems to be a sufficient reason
(albeit not the only reason) to assert that Dembski’s alleged fourth law of
thermodynamics, which is supposed to be a generalization of his LCI, makes no
sense.

No Free Lunch?

The title (No Free Lunch)
of Dembski’s 2002 book refers to certain theorems of optimization theory38
that Dembski asserts make evolution by a Darwinian path impossible. For example,
“The No Free Lunch theorems dash any hope of generating specified complexity via
evolutionary algorithms” (p.196). Similarly, “The No Free Lunch theorems show
that evolutionary algorithms, apart from careful fine-tuning by a programmer,
are no better than blind search and thus no better than pure chance” (p. 212),
and “The No Free Lunch theorems show that for evolutionary algorithms to output
CSI they had first to receive a prior input of CSI” (p. 223).

There are
several NFL theorems, the most relevant for our discussion being the “first NFL
theorem for search” (NFL-1).

At the heart
of the NFL theorems are two concepts: a fitness function (or its
opposite, a cost function), and search algorithms. A fitness
function (often a synonym of “figure of merit’) is a quantitative characteristic
of a system that is related to its functioning or its usefulness, or is of
interest for any other reason. For example, the fitness function may list the
heights of peaks in some mountainous region as a function of their locations.
The physical relief of the mountainous region is an example of a fitness
landscape. Imagine that we want to find the highest peak in that region.
Search algorithms are the sequences of steps to be taken in the search for the
highest peak. The search may be directed toward a certain target—say, a peak
which is 6,000 meters above the sea level. In this case the search is terminated
when the target, the 6,000 meter high peak, has been located. Equally the search
may not be directed toward a pre-selected target but may be conducted until,
say, a pre-selected number of peaks have been explored, and then the search is
terminated regardless of how tall the last conquered peak turns out to be.

NFL-1 is
equally valid for the first (targeted) and the second (non-targeted) searches.
Algorithms are strategies employed for the search. One strategy may be
climbing up peaks one by one, moving from the mountain region’s periphery toward
its geographic center, measuring the heights of the conquered peaks with an
altimeter and recording them. Another strategy may suggest climbing peak after
peak, selecting at each step a nearby peak that looks higher than the one
already conquered. These two strategies correspond to two different search
algorithms.

Although NFL-1
per se is not related to any performance measures, it may be convenient
to employ certain performance measures which, although not required by
the NFL theorem, may facilitate the judgment about the efficacy of an algorithm.
It is in principle unimportant how the data directly obtained in the search are
mapped onto the performance measure.38 The performance measure in a
search for a pre-selected peak (a targeted search), may be, for example, chosen
as the number of steps an algorithm needs to find the target. The algorithm that
finds the target in fewer steps “performs” better. The performance measure in a
non-targeted search which terminates after a pre-selected number of steps may
be, for example, the maximum height reached after the pre-selected number of
climbs. The algorithm that ends the search at a taller peak “performs” better.

NFL-1 per
se has nothing to do with the choice of a performance measure. It considers
the set of data obtained by the search algorithms and presented as a table
listing the measured values of the fitness function in temporal order. This
table is called a sample. NFL-1 relates to samples in probabilistic
terms. It says that the probabilities of obtaining a certain sample by two
different search algorithms are the same if these probabilities are averaged
over all possible fitness landscapes.

The word
averaged is crucial. NFL-1 asserts that no algorithm is better than any
other algorithm if their results are averaged over all possible fitness
functions. Thus, if a certain algorithm is better on a certain type of fitness
landscapes, it is necessarily worse on some other types of fitness landscapes.
Of critical importance, the NFL-1 theorem says nothing about any algorithms’
advantages or shortcomings on specific fitness landscapes, where one
algorithm may drastically outperform other algorithms at the cost of being inept
on some other fitness landscapes.

Now look at
how Dembski renders the gist of the NFL theorems:39

“A generic NFL theorem now takes the following form: It
sets up a performance measure M that characterizes how effectively an
evolutionary algorithm E locates a target T within m steps using information j.”

This
description is wrong in all of its parts. First, NFL-1 does not “set up a
performance measure.” No such quantity is mentioned in the theorem’s proof,
which is valid regardless of any performance measure. Second, NFL-1 does not
relate to any targets. It is equally valid for algorithms searching for a target
and for such algorithms that do not search for any pre-selected target. Third,
NFL-1 has nothing to do with the any information j which resides
outside the search space (this point will be discussed below in the section
about the “displacement problem”).

Dembski misuses the NFL
theorem when he asserts that evolutionary algorithms cannot outperform blind
search: “since blind search always constitutes a perfectly valid evolutionary
algorithm, this means that the average performance of any evolutionary algorithm
E is no better than blind search”39 Since blind search is an
extremely slow process, and no other algorithm can do better that blind search,
then, according to Dembski, evolutionary algorithms cannot ensure the rate of
evolution required by evolution theory, which entails random mutations plus
natural selection.

This statement
is misleading. Evolutionary algorithms indeed cannot outperform blind search
but, as Dembski knows, only if their performance is averaged over all
possible fitness functions. They can (and do) immensely outperform blind search
on specific fitness landscapes, both in computer simulations and in the real
biosphere. Dembski himself reviews examples of evolutionary algorithms immensely
outperforming blind search (or random sampling) — like Richard Dawkins’ “weasel
algorithm,”40 a checkers-playing algorithm41, and an
antenna-designing algorithm42 — but he forgets about them when
wrongly claiming that NFL-1 prohibits biological evolution.

Dembski admits
that evolutionary algorithms can outperform blind search if they are fine-tuned
by a programmer. He does not believe, though, that natural genetic algorithms
can be naturally fine-tuned to climb natural fitness landscapes.43
This disbelief, although it is Dembski’s prerogative, is not based on empirical
or logical foundation but only on Dembski’s philosophical/religious convictions.
It is possible, though, to show that the fitness landscapes encountered in the
real biosphere can often be fine-tuned to the available genetic algorithms based
on mutations and natural selection. Let us discuss it (see the following
section).

Displacement Problem

Chapter 4 in
Dembski’s No Free Lunch is devoted to NFL-1which allegedly proves the
impossibility of Darwinian evolution. Dembski’s thesis was strongly rebuffed by
a number of critics, including the co-author of the NFL theorems, David Wolpert.6
Confronted with critique, Dembski, without acknowledging his error, tried to
make it look inconsequential. Since he could not make chapter 4 in his book
disappear, he announced instead44 that, contrary to the obviously
triumphant appeal to the NFL theorems in his book, these theorems were not
really crucial for his thesis but just a particular example of what he calls
displacement problem (which, however, was in fact introduced in his book as
a consequence of his interpretation of the NFL theorems).

Here is
how Dembski defines the displacement problem:45

“…the problem of finding a given target has been displaced to the new problem of
finding the information j capable of locating that target. Our original
problem was finding a certain target within phase space. Our new problem is
finding a certain j within the information-resource space J.”

As noted
earlier, the real problem is not necessarily “finding a given target” because
search algorithms may work without being directed toward a “given target,” and
indeed, biological evolution is a process where there is no long-term target.

With his
habitual inconsistency, Dembski in some instances writes that evolutionary
algorithms are supposed to be “non teleological,” that is, not directed to a
target, and also that biological evolution is a process without a pre-selected
target. In other instances, however, he states the opposite — that evolution is
after all at least partially targeted. For example, “evolutionary
algorithms are supposed to be capable of solving complex problems without
invoking teleology.”46But later,
“An evolutionary algorithm is supposed to find a target within phase space.”
45 Since a search for a target is necessarily teleological, these two
statements are contradictory.

Furthermore,
all Dembski’s talk about information j sitting in an
“information-resource space J” is irrelevant to real life
problems. The NFL theorems are only valid for “black-box algorithms.” This means
that, before starting the search over the fitness landscape, the algorithm
incorporates no knowledge whatsoever about the landscape. It probes the
landscape one point at a time, gradually acquiring bits of information about the
landscape. The fitness landscape is always a given: the algorithm has no
choice of a fitness landscape but only explores an existing landscape it faces.
Therefore the displacement problem is a phantom.

Imagine an
organism existing in a certain environment. As a simplified example, let’s say
it is an animal that feeds on fruit growing on trees but has no tree-climbing
skill. If the animal is too small, it has problems in reaching the fruit and its
survival is uncertain. If it is too tall, it has problems in moving through the
dense jungle and thus reaching more trees. Hence we might expect a certain
optimal size for that animal, not too small and not too large, which provides
the best chance of surviving and thus having progeny. The performance measure in
this case can be, say, the number of descendants left by the animal, or the
duration of the animal’s life, or any other quantity that can be used to
characterize the animal’s chance for leaving more descendants. If we plot the
dependence of this quantity on the animal’s size, we will get a more-or-less
bell-shaped curve with a peak of fitness at a certain optimal size. This graph
is a simplified, two-dimensional model of a fitness landscape (which usually is
multi-dimensional). This fitness landscape is determined by the environment. The
animal has no choice of a fitness landscape — it is given. Search algorithms
(sequences of events and actions resulting in exploration of the fitness
landscape) that entail random mutations and natural selection fit in well with
this fitness landscape: those mutations which result in the animal’s size
approaching the optimal value will be naturally selected to ensure the
maximum fitness, that is, have the best chance of leaving progeny.

No excursion
into the “information-resource space” imagined by Dembski is required, so his
displacement problem is an abstract invention that does not exist in practice.

Conclusion

Dembski has either authored or
edited at least eight books and numerous articles, essays, and Internet posts.
He does not shy away from reproducing, often verbatim, the same passages time
and time again, apparently striving for having his ideas disseminated as widely
as possible, in every medium he can reach. On the other hand, encountering
criticisms, he sometimes surreptitiously modifies his argument (without ever
admitting error) so as to quietly slide out from the predicament caused by the
criticism.46

Because of the
large volume of Dembski’s publications, my review has necessarily had to be
cursory. I hope I have nevertheless shown that Dembski’s so highly acclaimed
achievements are just a nebulous dream; the real contents of his ideas and
notions are in an inverse relation to the intensity of the praise heaped upon
him by the ID crowd. If Dembski’s work is the best the ID advocates have to
show, then the entire ID enterprise is a political movement that wholly lacks
scientific significance. 47

(f) 2004. “There Is a Free Lunch
after All: William Dembski’s Wrong Answers to Irrelevant Questions.” Chapter 11
of M. Young and T. Edis, eds., Why Intelligent Design Fails: A Scientific
Critique of the New Creationism, New
Brunswick, N. J.: Rutgers University Press.

(d) 2000. “The Third Mode of
Explanation: Detecting Evidence of Intelligent Design in the Sciences.” In
W.A.Dembski, M. J. Behe, and S. C. Meyer, eds., Science and Evidence for
Design in the Universe. San Francisco: Ignatius Press.

20. Ratzsch, Nature, Design, and Science. The
example of a false positive produced by the EF given in this book (pp. 166-167)
is a case of driving on a desert road whose left side was flanked by a long
fence with a single small hole in it. A tumbleweed driven by wind happened to
cross the road in front of Ratzsch’s car and rolled precisely through the sole
tiny hole. The event had an exceedingly small probability and was “specified” in
Dembski’s sense (exactly as a hit of a bull’s-eye by an arrow in Dembski’s
favorite example). Dembski’s EF leads to the conclusion that the event in
question (tumbleweed rolling through the hole in the fence) was designed while
it obviously was due to chance; this is a false positive.